Libin Liu

[About Me]

Libin Liu

Assistant Professor

Center on Frontiers of Computing Studies
Peking University

Email: libin.liu [at] pku.edu.cn

I am an assistant professor at Center on Frontiers of Computing Studies, Peking University. Before joining Peking University, I was the Chief Scientist of DeepMotion Inc. I was a postdoctoral research fellow at Disney Research and the University of British Columbia. I recieved my Ph.D. degree in computer science in 2014 and my B.S. degree in mathematics and physics in 2009, both from Tsinghua University.

I am interested in character animation, physics-based simulation, motion control, and related areas such as optimal control, reinforcement learning, deep learning, and robotics. I put a lot of work into realizing various agile human motions on simulated characters and robots.

[Projects]

	Learning Basketball Dribbling Skills Using Trajectory Optimization and Deep Reinforcement Learning Libin Liu, Jessica K. Hodgins We present a method based on trajectory optimization and deep reinforcement learning for learning robust controllers for various basketball dribbling skills, such as dribbling between the legs, running, and crossovers. ACM Transactions on Graphics, Vol 37 Issue 4, Article 142 (SIGGRAPH 2018). (to appear) [Project Page] [Basketball.pdf 6.7MB] [Video 123MB]
	Learning to Schedule Control Fragments for Physics-Based Characters Using Deep Q-Learning Libin Liu, Jessica K. Hodgins We present a deep Q-learning based method for learning a scheduling scheme that reorders short control fragments as necessary at runtime to achieve robust control of challenging skills such as skateboarding. ACM Transactions on Graphics, Vol 36 Issue 3, Article 29. (presented at SIGGRAPH 2017) [Project Page] [Paper 1.5MB] [Video 157MB]
	Guided Learning of Control Graphs for Physics-Based Characters Libin Liu, Michiel van de Panne, KangKang Yin, We present a method for learning robust control graphs that support real-time physics-based simulation of multiple characters, each capable of a diverse range of movement skills. ACM Transactions on Graphics, Vol 35, Issue 2, Article 29. (presented at SIGGRAPH 2016) [Project Page] [Paper 8.9MB] [Video 117MB] [Slides 1.6MB] [I've fallen, but I can get up!]
	Learning Reduced-Order Feedback Policies for Motion Skills Kai Ding, Libin Liu, Michiel van de Panne, KangKang Yin, Proc. ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2015 (SCA Best Paper Award) [Project Page] [Paper 2.5MB]
	Deformation Capture and Modeling of Soft Objects Bin Wang, Longhua Wu, KangKang Yin, Uri Ascher, Libin Liu, Hui Huang. ACM Transactions on Graphics, Vol 34, Issue 4, Article 94 (SIGGRAPH 2015) [Project Page] [Paper 70MB]
	Improving Sampling-based Motion Control Libin Liu, KangKang Yin, Baining Guo. We address several limitations of the sampling-based motion control method. A variety of highly agile motions, ranging from stylized walking and dancing to gymnastic and Martial Arts routines, can be easily reconstructed now. Computer Graphics Forum 34(2) (Eurographics 2015). [Project Page] [Paper 3.8MB] [Video 59.8MB]
	Simulation and Control of Skeleton-driven Soft Body Characters Libin Liu, KangKang Yin, Bin Wang, Baining Guo. We present a physics-based framework for simulation and control of human-like skeleton-driven soft body characters. We propose a novel pose-based plasticity model to achieve large skin deformation around joints. We further reconstruct controls from reference trajectories captured from human subjects by augmenting a sampling-based algorithm. ACM Transactions on Graphics, Vol 32, Issue 6, Article 215 (SIGGRAPH Asia 2013) [Project Page] [Paper 6.4MB] [Video 106MB] [Slides 5.4MB]
	Terrain Runner: Control, Parameterization, Composition, and Planning for Highly Dynamic Motions Libin Liu, KangKang Yin, Michiel van de Panne, Baining Guo. We present methods for the control, parameterization, composition, and planning for highly dynamic motions. More specifically, we learn the skills required by real-time physics-based avatars to perform parkour-style fast terrain crossing using a mix of running, jumping, speed-vaulting, and drop-rolling. ACM Transactions on Graphics, Vol 31, Issue 6, Article 154 (SIGGRAPH Asia 2012) [Project Page] [Paper 40MB] [Video: Full 103MB] [Slides 2.9MB]
	Sampling-based Contact-rich Motion Control Libin Liu, KangKang Yin, Michiel van de Panne, Tianjia Shao Weiwei Xu. Given a motion capture trajectory, we propose to extract its control by randomized sampling. ACM Transactions on Graphics, Vol 29, Issue 4, Article 128 (SIGGRAPH 2010) [Project Page] [Paper 6.8MB] [Video 44.3MB (with audio)] [Slides 1.6MB]